This tutorial uses data and reproduces a subset of analyses reported in the following manuscript:
The COVID-19 pandemic sent shockwaves across the fabric of our society. Examining the impact of the pandemic on business leadership is particularly important to understanding how this event affected their decision-making. The present study documents the psychological effects of the COVID-19 pandemic on chief executive officers (CEOs). This was accomplished by analyzing CEOs’ language from quarterly earnings calls (N = 19,536) for a year before and after lockdown. CEOs had large shifts in language in the months immediately following the start of the pandemic lockdowns. Analytic thinking plummeted after the world went into lockdown, with CEOs’ language becoming less technical and more personal and intuitive. In parallel, CEOs’ language showed signs of increased cognitive load, as they were processing the effect of the pandemic on their business practices. Business leaders’ use of collective-focused language (we-usage) dropped substantially after the pandemic began, perhaps suggesting CEOs felt disconnected from their companies. Self-focused (I-usage) language increased, showing the increased preoccupation of business leaders. The size of the observed shifts in language during the pandemic also dwarfed responses to other events that occurred dating back to 2010, with the effect lasting around seven months.
palette_map = c("#3B9AB2", "#EBCC2A", "#F21A00")
palette_condition = c("#ee9b00", "#bb3e03", "#005f73")
plot_aes = theme_classic() +
theme(text = element_text(size = 16, family = "Futura Medium")) +
theme(axis.text.x=element_text(angle=45, hjust=1)) +
theme(plot.title.position = 'plot',
plot.title = element_text(hjust = 0.5, face = "bold", size = 16)) +
theme(axis.text=element_text(size=16),
axis.title=element_text(size=20,face="bold"))+
theme(plot.title.position = 'plot',
plot.title = element_text(hjust = 0.5, face = "bold", size = 20)) +
theme(axis.text=element_text(size = 14),
axis.title=element_text(size = 20,face="bold"))baseline_ttest <- function(ttest_list) {
# Extract relevant information from each test and store in a data frame
ttest_df <- data.frame(
Group1 = seq(0,0,1),
Group2 = seq(1,24,1),
t = sapply(ttest_list, function(x) x$statistic),
df = sapply(ttest_list, function(x) x$parameter),
p_value = sapply(ttest_list, function(x) x$p.value)
)
# Format p-values as scientific notation
ttest_df$p_value <- format(ttest_df$p_value, scientific = T)
# Rename columns
colnames(ttest_df) <- c("t", "t + 1 ", "t-value", "Degrees of Freedom", "p-value")
# Create table using kableExtra
kable(ttest_df, caption = "Summary of Welch's t-Tests", booktabs = TRUE) %>%
kableExtra::kable_styling()
}
post_pandemic_summary <- function(ttest_list) {
# Extract relevant information from each test and store in a data frame
ttest_df <- data.frame(
Group1 = seq(12,23,1),
Group2 = seq(13,24,1),
t = sapply(ttest_list, function(x) x$statistic),
df = sapply(ttest_list, function(x) x$parameter),
p_value = sapply(ttest_list, function(x) x$p.value)
)
# Format p-values as scientific notation
ttest_df$p_value <- format(ttest_df$p_value, scientific = T)
# Rename columns
colnames(ttest_df) <- c("t", "t + 1 ", "t-value", "Degrees of Freedom", "p-value")
# Create table using kableExtra
kable(ttest_df, caption = "Summary of Welch's t-Tests", booktabs = TRUE) %>%
kableExtra::kable_styling()
}
baseline_cohen_d <- function(cohen_d_list) {
# Extract relevant information from each test and store in a data frame
cohen_d_df <- data.frame(
Group1 = seq(0,0,1),
Group2 = seq(1,24,1),
Cohen_d = sapply(cohen_d_list, function(x) x$estimate)
)
# Rename columns
colnames(cohen_d_df) <- c("t", "t + 1", "Cohen's d")
# Create table using kableExtra
kable(cohen_d_df, caption = "Summary of Cohen's D", booktabs = TRUE) %>%
kableExtra::kable_styling()
}
post_cohen_d <- function(cohen_d_list) {
# Extract relevant information from each test and store in a data frame
cohen_d_df <- data.frame(
Group1 = seq(12,23,1),
Group2 = seq(13,24,1),
Cohen_d = sapply(cohen_d_list, function(x) x$estimate)
)
# Rename columns
colnames(cohen_d_df) <- c("t", "t+1", "Cohen's d")
# Create table using kableExtra
kable(cohen_d_df, caption = "Summary of Cohen's D", booktabs = TRUE) %>%
kableExtra::kable_styling()
}
baseline_mean_diff <- function(mean_diff_list) {
# Extract relevant information from each mean difference calculation and store in a data frame
mean_diff_df <- data.frame(
Group1 = seq(0,0,1),
Group2 = seq(1,24,1),
mean_diff = mean_diff_list
)
# Rename columns
colnames(mean_diff_df) <- c("t", "t+1", "Mean Difference")
# Create table using kableExtra
kable(mean_diff_df, caption = "Summary of Mean Differences", booktabs = TRUE) %>%
kableExtra::kable_styling()
}
post_mean_diff <- function(mean_diff_list) {
# Extract relevant information from each mean difference calculation and store in a data frame
mean_diff_df <- data.frame(
Group1 = seq(12,23,1),
Group2 = seq(13,24,1),
mean_diff = mean_diff_list
)
# Rename columns
colnames(mean_diff_df) <- c("t", "t+1", "Mean Difference")
# Create table using kableExtra
kable(mean_diff_df, caption = "Summary of Mean Differences", booktabs = TRUE) %>%
kableExtra::kable_styling()
}data <- read_csv("Big_CEO.csv") #read in the data
data <- data["2019-03-01"<= data$Date & data$Date <= "2021-04-01",] #subsetting covid dates
data <- data %>% filter(WC<=5400) %>% #filter out based on our exclusion criteria
filter(WC>=25)
data$month_year <- format(as.Date(data$Date), "%Y-%m") #reformat
data_tidy <- data %>% dplyr::select(Date, Speaker, Analytic, cogproc,allnone,we,i,emo_anx) %>%
mutate(Date = lubridate::ymd(Date),
time_month = as.numeric(Date - ymd("2019-03-01")) / 30, #centering at start of march
time_month_quad = time_month * time_month) #making our quadratic term
data_tidy$Date_off <- floor(data_tidy$time_month) #rounding off dates to whole months using ceiling function (0 = 2019-03, 24 = 2021-04)
data_tidy$Date_covid <- as.factor(data_tidy$Date_off) #factorizedf <- read_csv("Big_CEO.csv")#put code here to read in Big CEO data
df <- df %>% filter(WC<=5400) %>%
filter(WC>=25)
df$month_year <- format(as.Date(df$Date), "%Y-%m") ###extracting month and year to build fiscal quarter graphs, need a new variable bc if not it'll give us issues
df2 <- df %>%#converting our dates to quarterly dates
group_by(month_year) %>% ###grouping by the Top100 tag and date
summarise_at(vars("Date","WC","Analytic","cogproc",'we','i'), funs(mean, std.error),) #pulling the means and SEs for our variables of interest
df2 <- df2["2019-01"<= df2$month_year & df2$month_year <= "2021-03",] #covid dates We were interested in how language changed relative to baseline one year pre-pandemic, as well as how language changed after the Pandemic.
As a result we ran two separate set of analyses comparing t(time zero) to t[i] and t(12 months after our centered data point) to t + 1. The groups you see will be centered on 03/2019. That is, 12 = 03/2020, 13 = 04,2020, etc. etc.
analytic_my.t = function(fac1, fac2){
t.test(data_tidy$Analytic[data_tidy$Date_covid==fac1],
data_tidy$Analytic[data_tidy$Date_covid==fac2])
} #writing our t-test function to compare t to t[i]
analytic_my.d = function(fac1, fac2){
cohen.d(data_tidy$Analytic[data_tidy$Date_covid==fac1],
data_tidy$Analytic[data_tidy$Date_covid==fac2])
} #function for cohen's d
analytic_mean <- function(fac1, fac2){
mean(data_tidy$Analytic[data_tidy$Date_covid==fac1])-
mean(data_tidy$Analytic[data_tidy$Date_covid==fac2])
} #function to do mean differencescogproc_my.t = function(fac1, fac2){
t.test(data_tidy$cogproc[data_tidy$Date_covid==fac1],
data_tidy$cogproc[data_tidy$Date_covid==fac2])
} #writing our t-test function to compare t to t[i]
cogproc_my.d = function(fac1, fac2){
cohen.d(data_tidy$cogproc[data_tidy$Date_covid==fac1],
data_tidy$cogproc[data_tidy$Date_covid==fac2])
} #function for cohen's d
cogproc_mean <- function(fac1, fac2){
mean(data_tidy$cogproc[data_tidy$Date_covid==fac1])-
mean(data_tidy$cogproc[data_tidy$Date_covid==fac2])
} #function to do mean differencesi_my.t = function(fac1, fac2){
t.test(data_tidy$i[data_tidy$Date_covid==fac1],
data_tidy$i[data_tidy$Date_covid==fac2])
} #writing our t-test function to compare t to t + 1
i_my.d = function(fac1, fac2){
cohen.d(data_tidy$i[data_tidy$Date_covid==fac1],
data_tidy$i[data_tidy$Date_covid==fac2])
} #function for cohen's d
i_mean <- function(fac1, fac2){
mean(data_tidy$i[data_tidy$Date_covid==fac1])-
mean(data_tidy$i[data_tidy$Date_covid==fac2])
} #function to do mean differenceswe_my.t = function(fac1, fac2){
t.test(data_tidy$we[data_tidy$Date_covid==fac1],
data_tidy$we[data_tidy$Date_covid==fac2])
}
we_my.d = function(fac1, fac2){
cohen.d(data_tidy$we[data_tidy$Date_covid==fac1],
data_tidy$we[data_tidy$Date_covid==fac2])
} #function for cohen's d
we_mean <- function(fac1, fac2){
mean(data_tidy$we[data_tidy$Date_covid==fac1])-
mean(data_tidy$we[data_tidy$Date_covid==fac2])
} #function to do mean differencesData transformations
Exclusions
range(data$Date)## [1] "2019-03-01" "2021-04-01"
speakers <- data %>%
select(Speaker) %>%
unique() %>%
dplyr::summarize(n = n()) %>%
reactable::reactable(striped = TRUE)
speakerstranscripts <- data %>%
select(1) %>%
dplyr::summarize(n = n()) %>%
reactable::reactable(striped = TRUE)
transcriptsword_count <- data %>%
select(WC) %>%
dplyr::summarize(mean = mean(WC)) %>%
reactable::reactable(striped = TRUE)
word_countanalytic_ttest<- mapply(analytic_my.t,seq(12,23,1), seq(13,24,1),SIMPLIFY=F) #compare t (first parantheses) to t[i] (second parentheses)increasing by 1
post_pandemic_summary(analytic_ttest)| t | t + 1 | t-value | Degrees of Freedom | p-value |
|---|---|---|---|---|
| 12 | 13 | 5.0848767 | 525.79320 | 5.124345e-07 |
| 13 | 14 | -2.5947675 | 373.06404 | 9.838752e-03 |
| 14 | 15 | -1.6725600 | 252.03534 | 9.565479e-02 |
| 15 | 16 | 1.9241684 | 377.61680 | 5.508471e-02 |
| 16 | 17 | -2.2121608 | 200.57005 | 2.808412e-02 |
| 17 | 18 | -1.6872236 | 218.93267 | 9.298455e-02 |
| 18 | 19 | 0.6199376 | 262.60906 | 5.358364e-01 |
| 19 | 20 | 0.8737898 | 128.21711 | 3.838664e-01 |
| 20 | 21 | -1.5397962 | 230.75591 | 1.249802e-01 |
| 21 | 22 | 1.9533418 | 94.31745 | 5.374259e-02 |
| 22 | 23 | -1.1497811 | 55.55164 | 2.551600e-01 |
| 23 | 24 | -1.7179003 | 2141.37219 | 8.595937e-02 |
analytic_d <- mapply(analytic_my.d,seq(12,23,1), seq(13,24,1),SIMPLIFY=FALSE)
post_cohen_d(analytic_d)| t | t+1 | Cohen’s d |
|---|---|---|
| 12 | 13 | 0.3274589 |
| 13 | 14 | -0.1597933 |
| 14 | 15 | -0.1320224 |
| 15 | 16 | 0.1935631 |
| 16 | 17 | -0.1616992 |
| 17 | 18 | -0.1481301 |
| 18 | 19 | 0.0709701 |
| 19 | 20 | 0.0898748 |
| 20 | 21 | -0.1246402 |
| 21 | 22 | 0.2681803 |
| 22 | 23 | -0.1598304 |
| 23 | 24 | -0.0739462 |
analytic_meandiff <- mapply(analytic_mean, seq(12,23,1), seq(13,24,1)) #across all of the months comparing to time zero
post_mean_diff(analytic_meandiff)| t | t+1 | Mean Difference |
|---|---|---|
| 12 | 13 | 4.734622 |
| 13 | 14 | -2.190455 |
| 14 | 15 | -1.844328 |
| 15 | 16 | 2.748318 |
| 16 | 17 | -2.231753 |
| 17 | 18 | -2.101267 |
| 18 | 19 | 1.158869 |
| 19 | 20 | 1.276462 |
| 20 | 21 | -1.779122 |
| 21 | 22 | 4.065080 |
| 22 | 23 | -2.075629 |
| 23 | 24 | -0.994088 |
cogproc_ttest <-mapply(cogproc_my.t, seq(12,23,1), seq(13,24,1),SIMPLIFY=FALSE) #compare t (first parathese) to t[i] (second parantheses) increasing by 1
post_pandemic_summary(cogproc_ttest)| t | t + 1 | t-value | Degrees of Freedom | p-value |
|---|---|---|---|---|
| 12 | 13 | -4.3160945 | 534.57336 | 1.892660e-05 |
| 13 | 14 | 1.4046015 | 366.53625 | 1.609866e-01 |
| 14 | 15 | 4.0193476 | 257.86515 | 7.665356e-05 |
| 15 | 16 | -3.1317117 | 367.29975 | 1.877275e-03 |
| 16 | 17 | 0.9867920 | 199.23919 | 3.249415e-01 |
| 17 | 18 | 4.1803820 | 223.61017 | 4.177506e-05 |
| 18 | 19 | -1.1984064 | 285.88282 | 2.317513e-01 |
| 19 | 20 | -1.4929615 | 133.61894 | 1.378047e-01 |
| 20 | 21 | 3.2109343 | 234.84605 | 1.508000e-03 |
| 21 | 22 | -1.7045407 | 87.34608 | 9.183489e-02 |
| 22 | 23 | 0.9967763 | 55.37573 | 3.232089e-01 |
| 23 | 24 | -0.9994281 | 2145.12748 | 3.177001e-01 |
cogproc_d <-mapply(cogproc_my.d, seq(12,23,1), seq(13,24,1),SIMPLIFY=FALSE)
post_cohen_d(cogproc_d)| t | t+1 | Cohen’s d |
|---|---|---|
| 12 | 13 | -0.2755415 |
| 13 | 14 | 0.0887056 |
| 14 | 15 | 0.3007241 |
| 15 | 16 | -0.3204553 |
| 16 | 17 | 0.0732556 |
| 17 | 18 | 0.3435609 |
| 18 | 19 | -0.1329353 |
| 19 | 20 | -0.1294167 |
| 20 | 21 | 0.2476709 |
| 21 | 22 | -0.2453381 |
| 22 | 23 | 0.1405453 |
| 23 | 24 | -0.0429758 |
cogproc_meandiff <- mapply(cogproc_mean, seq(12,23,1), seq(13,24,1)) # comparing time zero [3/2019]across all of the months
post_mean_diff(cogproc_meandiff)| t | t+1 | Mean Difference |
|---|---|---|
| 12 | 13 | -0.6107287 |
| 13 | 14 | 0.1784774 |
| 14 | 15 | 0.6094504 |
| 15 | 16 | -0.6540232 |
| 16 | 17 | 0.1559844 |
| 17 | 18 | 0.7442075 |
| 18 | 19 | -0.2962170 |
| 19 | 20 | -0.2746360 |
| 20 | 21 | 0.5304979 |
| 21 | 22 | -0.5357971 |
| 22 | 23 | 0.2775877 |
| 23 | 24 | -0.0886600 |
i_ttest <- mapply(i_my.t, seq(12,23,1), seq(13,24,1),SIMPLIFY=FALSE) #compare t (first paratheses) to t[i] (second parentheses) increasing by 1
post_pandemic_summary(i_ttest)| t | t + 1 | t-value | Degrees of Freedom | p-value |
|---|---|---|---|---|
| 12 | 13 | -5.1026305 | 477.85082 | 4.841738e-07 |
| 13 | 14 | 2.9682570 | 362.96961 | 3.193717e-03 |
| 14 | 15 | 2.7352278 | 261.20479 | 6.660709e-03 |
| 15 | 16 | -3.5894844 | 336.98113 | 3.805206e-04 |
| 16 | 17 | 1.7614255 | 191.52014 | 7.976208e-02 |
| 17 | 18 | 3.4393905 | 240.73312 | 6.870032e-04 |
| 18 | 19 | -2.6019091 | 255.11065 | 9.812584e-03 |
| 19 | 20 | 0.4503223 | 134.90596 | 6.532009e-01 |
| 20 | 21 | 1.5059378 | 248.77332 | 1.333518e-01 |
| 21 | 22 | 2.0158644 | 84.28386 | 4.699962e-02 |
| 22 | 23 | -3.8068297 | 57.55886 | 3.436805e-04 |
| 23 | 24 | 4.4094793 | 2135.84040 | 1.087616e-05 |
i_d <- mapply(i_my.d,seq(12,23,1), seq(13,24,1),SIMPLIFY=FALSE)
post_cohen_d(i_d)| t | t+1 | Cohen’s d |
|---|---|---|
| 12 | 13 | -0.3467518 |
| 13 | 14 | 0.1902125 |
| 14 | 15 | 0.1990807 |
| 15 | 16 | -0.3757604 |
| 16 | 17 | 0.1451672 |
| 17 | 18 | 0.2369631 |
| 18 | 19 | -0.3007221 |
| 19 | 20 | 0.0377993 |
| 20 | 21 | 0.1020099 |
| 21 | 22 | 0.2971566 |
| 22 | 23 | -0.4621942 |
| 23 | 24 | 0.1900173 |
i_meandiff <- mapply(i_mean,seq(12,23,1), seq(13,24,1)) # comparing time zero [3/2020]across all of the months
post_mean_diff(i_meandiff)| t | t+1 | Mean Difference |
|---|---|---|
| 12 | 13 | -0.2878044 |
| 13 | 14 | 0.1550533 |
| 14 | 15 | 0.1624754 |
| 15 | 16 | -0.3241516 |
| 16 | 17 | 0.1289192 |
| 17 | 18 | 0.2083141 |
| 18 | 19 | -0.2363725 |
| 19 | 20 | 0.0329017 |
| 20 | 21 | 0.0885966 |
| 21 | 22 | 0.2292627 |
| 22 | 23 | -0.3911951 |
| 23 | 24 | 0.1657095 |
we_ttest <- mapply(we_my.t, seq(12,23,1), seq(13,24,1),SIMPLIFY=FALSE) #compare t (first parathese) to t[i] (second parantheses) increasing by 1
post_pandemic_summary(we_ttest)| t | t + 1 | t-value | Degrees of Freedom | p-value |
|---|---|---|---|---|
| 12 | 13 | 4.1037791 | 527.07583 | 4.708824e-05 |
| 13 | 14 | 0.9116989 | 378.81928 | 3.625070e-01 |
| 14 | 15 | -3.3226285 | 253.13940 | 1.023448e-03 |
| 15 | 16 | 2.4647106 | 373.96103 | 1.416113e-02 |
| 16 | 17 | -0.3375119 | 197.51750 | 7.360894e-01 |
| 17 | 18 | -4.2758502 | 229.49548 | 2.793946e-05 |
| 18 | 19 | 2.5509775 | 262.60210 | 1.130991e-02 |
| 19 | 20 | -0.1421962 | 131.79434 | 8.871422e-01 |
| 20 | 21 | -1.9395335 | 238.21223 | 5.361708e-02 |
| 21 | 22 | -0.2952385 | 84.06212 | 7.685396e-01 |
| 22 | 23 | 0.8556597 | 55.76358 | 3.958478e-01 |
| 23 | 24 | -0.3495394 | 2137.76534 | 7.267188e-01 |
we_d <- mapply(we_my.d, seq(12,23,1), seq(13,24,1),SIMPLIFY=FALSE)
post_cohen_d(we_d)| t | t+1 | Cohen’s d |
|---|---|---|
| 12 | 13 | 0.2639367 |
| 13 | 14 | 0.0549934 |
| 14 | 15 | -0.2594704 |
| 15 | 16 | 0.2501259 |
| 16 | 17 | -0.0255875 |
| 17 | 18 | -0.3276203 |
| 18 | 19 | 0.2920369 |
| 19 | 20 | -0.0129636 |
| 20 | 21 | -0.1443587 |
| 21 | 22 | -0.0435999 |
| 22 | 23 | 0.1169953 |
| 23 | 24 | -0.0150573 |
we_meandiff <- mapply(we_mean, seq(12,23,1), seq(13,24,1)) # comparing time zero [3/2020]across all of the months
post_mean_diff(we_meandiff)| t | t+1 | Mean Difference |
|---|---|---|
| 12 | 13 | 0.3777932 |
| 13 | 14 | 0.0763380 |
| 14 | 15 | -0.3676046 |
| 15 | 16 | 0.3649285 |
| 16 | 17 | -0.0365235 |
| 17 | 18 | -0.4710551 |
| 18 | 19 | 0.4168557 |
| 19 | 20 | -0.0182846 |
| 20 | 21 | -0.2041654 |
| 21 | 22 | -0.0608833 |
| 22 | 23 | 0.1582888 |
| 23 | 24 | -0.0209555 |
analytic_ttest_baseline <-mapply(analytic_my.t,0, seq(1,24,1),SIMPLIFY=FALSE) #compare t (first parantheses) to t[i] (second parentheses)increasing by 1
baseline_ttest(analytic_ttest_baseline)| t | t + 1 | t-value | Degrees of Freedom | p-value |
|---|---|---|---|---|
| 0 | 1 | 1.5025175 | 1161.46333 | 1.332353e-01 |
| 0 | 2 | 0.6860139 | 1036.84856 | 4.928577e-01 |
| 0 | 3 | 0.2507930 | 245.14330 | 8.021842e-01 |
| 0 | 4 | 2.6728372 | 1120.10414 | 7.630544e-03 |
| 0 | 5 | 0.4785480 | 1004.80108 | 6.323643e-01 |
| 0 | 6 | 1.0343183 | 280.42507 | 3.018785e-01 |
| 0 | 7 | 2.6674817 | 1049.94370 | 7.759826e-03 |
| 0 | 8 | 1.4045584 | 993.35140 | 1.604652e-01 |
| 0 | 9 | 1.0147460 | 328.09331 | 3.109746e-01 |
| 0 | 10 | 1.5505263 | 286.24028 | 1.221201e-01 |
| 0 | 11 | 1.9737798 | 1061.63898 | 4.866575e-02 |
| 0 | 12 | 1.3053906 | 1272.10102 | 1.919959e-01 |
| 0 | 13 | 5.7769548 | 623.93692 | 1.200948e-08 |
| 0 | 14 | 5.1516350 | 929.47739 | 3.153290e-07 |
| 0 | 15 | 1.4218984 | 370.16499 | 1.558977e-01 |
| 0 | 16 | 3.9258380 | 316.92397 | 1.060657e-04 |
| 0 | 17 | 3.2571976 | 918.08556 | 1.166437e-03 |
| 0 | 18 | 0.1171218 | 302.23369 | 9.068413e-01 |
| 0 | 19 | 0.8462858 | 164.42299 | 3.986233e-01 |
| 0 | 20 | 3.7364297 | 920.43945 | 1.981471e-04 |
| 0 | 21 | 0.6393118 | 331.79337 | 5.230612e-01 |
| 0 | 22 | 2.6168435 | 63.20064 | 1.108971e-02 |
| 0 | 23 | 3.7686675 | 1111.95144 | 1.727388e-04 |
| 0 | 24 | 2.4325523 | 1125.18803 | 1.514789e-02 |
analytic_D_baseline <- mapply(analytic_my.d,0, seq(1,24,1),SIMPLIFY=FALSE)
baseline_cohen_d(analytic_D_baseline)| t | t + 1 | Cohen’s d |
|---|---|---|
| 0 | 1 | 0.0879752 |
| 0 | 2 | 0.0329980 |
| 0 | 3 | 0.0206107 |
| 0 | 4 | 0.1587215 |
| 0 | 5 | 0.0235235 |
| 0 | 6 | 0.0867045 |
| 0 | 7 | 0.1620807 |
| 0 | 8 | 0.0687147 |
| 0 | 9 | 0.0805849 |
| 0 | 10 | 0.1282654 |
| 0 | 11 | 0.1023933 |
| 0 | 12 | 0.0694416 |
| 0 | 13 | 0.3954264 |
| 0 | 14 | 0.2534133 |
| 0 | 15 | 0.1138341 |
| 0 | 16 | 0.3057368 |
| 0 | 17 | 0.1588173 |
| 0 | 18 | 0.0101558 |
| 0 | 19 | 0.0861013 |
| 0 | 20 | 0.1802980 |
| 0 | 21 | 0.0529819 |
| 0 | 22 | 0.3237240 |
| 0 | 23 | 0.2018620 |
| 0 | 24 | 0.1262979 |
analytic_mean_baseline <- mapply(analytic_mean, 0, seq(1,24,1)) #across all of the months comparing to time zero
baseline_mean_diff(analytic_mean_baseline)| t | t+1 | Mean Difference |
|---|---|---|
| 0 | 1 | 1.3114081 |
| 0 | 2 | 0.4935284 |
| 0 | 3 | 0.3039970 |
| 0 | 4 | 2.3251490 |
| 0 | 5 | 0.3411544 |
| 0 | 6 | 1.3027809 |
| 0 | 7 | 2.3954214 |
| 0 | 8 | 0.9976299 |
| 0 | 9 | 1.1986758 |
| 0 | 10 | 1.9188652 |
| 0 | 11 | 1.4369448 |
| 0 | 12 | 1.0438407 |
| 0 | 13 | 5.7784625 |
| 0 | 14 | 3.5880071 |
| 0 | 15 | 1.7436794 |
| 0 | 16 | 4.4919977 |
| 0 | 17 | 2.2602447 |
| 0 | 18 | 0.1589776 |
| 0 | 19 | 1.3178462 |
| 0 | 20 | 2.5943085 |
| 0 | 21 | 0.8151869 |
| 0 | 22 | 4.8802673 |
| 0 | 23 | 2.8046380 |
| 0 | 24 | 1.8105501 |
cogproc_ttest_baseline <- mapply(cogproc_my.t, 0, seq(1,24,1),SIMPLIFY=FALSE) #compare t (first parathese) to t[i] (second parantheses) increasing by 1
baseline_ttest(cogproc_ttest_baseline)| t | t + 1 | t-value | Degrees of Freedom | p-value |
|---|---|---|---|---|
| 0 | 1 | -0.5097155 | 1156.50973 | 6.103480e-01 |
| 0 | 2 | -0.7178587 | 1035.96962 | 4.730063e-01 |
| 0 | 3 | -0.2391309 | 218.72044 | 8.112280e-01 |
| 0 | 4 | -1.8416817 | 1119.69687 | 6.578607e-02 |
| 0 | 5 | -0.3763500 | 1051.93803 | 7.067326e-01 |
| 0 | 6 | 0.2442296 | 282.79380 | 8.072301e-01 |
| 0 | 7 | -1.7141683 | 1029.21251 | 8.679890e-02 |
| 0 | 8 | -0.9538148 | 1076.64206 | 3.403915e-01 |
| 0 | 9 | 1.0445702 | 320.30692 | 2.970093e-01 |
| 0 | 10 | -0.8168779 | 255.25892 | 4.147599e-01 |
| 0 | 11 | -0.7245359 | 1147.57474 | 4.688845e-01 |
| 0 | 12 | -2.0279981 | 1307.90475 | 4.276280e-02 |
| 0 | 13 | -5.7012479 | 609.24510 | 1.854777e-08 |
| 0 | 14 | -6.5910797 | 924.04251 | 7.328808e-11 |
| 0 | 15 | -0.3855580 | 395.99482 | 7.000311e-01 |
| 0 | 16 | -4.0811802 | 298.22073 | 5.758392e-05 |
| 0 | 17 | -5.4650159 | 949.00294 | 5.916345e-08 |
| 0 | 18 | 0.9264753 | 310.66778 | 3.549182e-01 |
| 0 | 19 | -0.5797074 | 184.73797 | 5.628182e-01 |
| 0 | 20 | -3.7993649 | 936.80835 | 1.544264e-04 |
| 0 | 21 | 0.7639021 | 341.61474 | 4.454529e-01 |
| 0 | 22 | -1.3820415 | 61.97292 | 1.719203e-01 |
| 0 | 23 | -1.0690564 | 1140.02341 | 2.852706e-01 |
| 0 | 24 | -1.8593187 | 1172.33479 | 6.323237e-02 |
cogproc_D_baseline <- mapply(cogproc_my.d, 0, seq(1,24,1),SIMPLIFY=FALSE)
baseline_cohen_d(cogproc_D_baseline)| t | t + 1 | Cohen’s d |
|---|---|---|
| 0 | 1 | -0.0298959 |
| 0 | 2 | -0.0345459 |
| 0 | 3 | -0.0213194 |
| 0 | 4 | -0.1093919 |
| 0 | 5 | -0.0180369 |
| 0 | 6 | 0.0203613 |
| 0 | 7 | -0.1048291 |
| 0 | 8 | -0.0445936 |
| 0 | 9 | 0.0841121 |
| 0 | 10 | -0.0731906 |
| 0 | 11 | -0.0364241 |
| 0 | 12 | -0.1070381 |
| 0 | 13 | -0.3938811 |
| 0 | 14 | -0.3255788 |
| 0 | 15 | -0.0297828 |
| 0 | 16 | -0.3291694 |
| 0 | 17 | -0.2601030 |
| 0 | 18 | 0.0788773 |
| 0 | 19 | -0.0527050 |
| 0 | 20 | -0.1809343 |
| 0 | 21 | 0.0622160 |
| 0 | 22 | -0.1777619 |
| 0 | 23 | -0.0568265 |
| 0 | 24 | -0.0951265 |
cogproc_mean_baseline <- mapply(cogproc_mean, 0, seq(1,24,1)) # comparing time zero [3/2020]across all of the months
baseline_mean_diff(cogproc_meandiff)| t | t+1 | Mean Difference |
|---|---|---|
| 0 | 1 | -0.6107287 |
| 0 | 2 | 0.1784774 |
| 0 | 3 | 0.6094504 |
| 0 | 4 | -0.6540232 |
| 0 | 5 | 0.1559844 |
| 0 | 6 | 0.7442075 |
| 0 | 7 | -0.2962170 |
| 0 | 8 | -0.2746360 |
| 0 | 9 | 0.5304979 |
| 0 | 10 | -0.5357971 |
| 0 | 11 | 0.2775877 |
| 0 | 12 | -0.0886600 |
| 0 | 13 | -0.6107287 |
| 0 | 14 | 0.1784774 |
| 0 | 15 | 0.6094504 |
| 0 | 16 | -0.6540232 |
| 0 | 17 | 0.1559844 |
| 0 | 18 | 0.7442075 |
| 0 | 19 | -0.2962170 |
| 0 | 20 | -0.2746360 |
| 0 | 21 | 0.5304979 |
| 0 | 22 | -0.5357971 |
| 0 | 23 | 0.2775877 |
| 0 | 24 | -0.0886600 |
i_ttest_baseline <- mapply(i_my.t, 0, seq(1,24,1),SIMPLIFY=FALSE) #compare t (first paratheseses) to t[i] (second parentheses) increasing by 1
baseline_ttest(i_ttest_baseline)| t | t + 1 | t-value | Degrees of Freedom | p-value |
|---|---|---|---|---|
| 0 | 1 | -3.3449936 | 1143.81760 | 8.495412e-04 |
| 0 | 2 | -1.1963077 | 1155.18280 | 2.318220e-01 |
| 0 | 3 | -0.1911368 | 213.55326 | 8.486000e-01 |
| 0 | 4 | -4.1439455 | 1114.30669 | 3.672274e-05 |
| 0 | 5 | -0.6476795 | 1056.55931 | 5.173329e-01 |
| 0 | 6 | -1.6111266 | 278.02962 | 1.082868e-01 |
| 0 | 7 | -3.3533234 | 1035.23122 | 8.273950e-04 |
| 0 | 8 | -2.0582130 | 1066.95830 | 3.981213e-02 |
| 0 | 9 | -1.4167584 | 265.19170 | 1.577272e-01 |
| 0 | 10 | -2.7747321 | 284.30487 | 5.890772e-03 |
| 0 | 11 | -1.9848824 | 1154.30486 | 4.739397e-02 |
| 0 | 12 | -0.3319999 | 1263.49829 | 7.399444e-01 |
| 0 | 13 | -5.0279656 | 571.48514 | 6.644118e-07 |
| 0 | 14 | -3.7092732 | 958.88047 | 2.197939e-04 |
| 0 | 15 | 0.2214347 | 390.57770 | 8.248697e-01 |
| 0 | 16 | -3.9254650 | 253.43509 | 1.115955e-04 |
| 0 | 17 | -4.4733002 | 1005.42169 | 8.580050e-06 |
| 0 | 18 | 0.4134953 | 350.62439 | 6.794966e-01 |
| 0 | 19 | -2.6459757 | 180.59824 | 8.864330e-03 |
| 0 | 20 | -4.3779164 | 986.11105 | 1.326045e-05 |
| 0 | 21 | -1.3221651 | 371.12983 | 1.869275e-01 |
| 0 | 22 | 1.3507586 | 63.33638 | 1.815790e-01 |
| 0 | 23 | -5.6222563 | 1250.83820 | 2.322252e-08 |
| 0 | 24 | -1.8930601 | 1254.79690 | 5.857980e-02 |
i_D_baseline <- mapply(i_my.d, 0, seq(1,24,1),SIMPLIFY=FALSE)
baseline_cohen_d(i_D_baseline)| t | t + 1 | Cohen’s d |
|---|---|---|
| 0 | 1 | -0.1965974 |
| 0 | 2 | -0.0543981 |
| 0 | 3 | -0.0173720 |
| 0 | 4 | -0.2467407 |
| 0 | 5 | -0.0309676 |
| 0 | 6 | -0.1358241 |
| 0 | 7 | -0.2047181 |
| 0 | 8 | -0.0966976 |
| 0 | 9 | -0.1296303 |
| 0 | 10 | -0.2305339 |
| 0 | 11 | -0.0995545 |
| 0 | 12 | -0.0176937 |
| 0 | 13 | -0.3562055 |
| 0 | 14 | -0.1785725 |
| 0 | 15 | 0.0172266 |
| 0 | 16 | -0.3536629 |
| 0 | 17 | -0.2047237 |
| 0 | 18 | 0.0327380 |
| 0 | 19 | -0.2453415 |
| 0 | 20 | -0.2010721 |
| 0 | 21 | -0.1028381 |
| 0 | 22 | 0.1664219 |
| 0 | 23 | -0.2903836 |
| 0 | 24 | -0.0945412 |
i_mean_baseline <- mapply(i_mean, 0, seq(1,24,1)) # comparing time zero [3/2020]across all of the months
baseline_mean_diff(i_mean_baseline)| t | t+1 | Mean Difference |
|---|---|---|
| 0 | 1 | -0.1747670 |
| 0 | 2 | -0.0504304 |
| 0 | 3 | -0.0148774 |
| 0 | 4 | -0.2082233 |
| 0 | 5 | -0.0265697 |
| 0 | 6 | -0.1159251 |
| 0 | 7 | -0.1744079 |
| 0 | 8 | -0.0846426 |
| 0 | 9 | -0.1162156 |
| 0 | 10 | -0.1958046 |
| 0 | 11 | -0.0842683 |
| 0 | 12 | -0.0149918 |
| 0 | 13 | -0.3027962 |
| 0 | 14 | -0.1477429 |
| 0 | 15 | 0.0147325 |
| 0 | 16 | -0.3094191 |
| 0 | 17 | -0.1804999 |
| 0 | 18 | 0.0278142 |
| 0 | 19 | -0.2085583 |
| 0 | 20 | -0.1756567 |
| 0 | 21 | -0.0870600 |
| 0 | 22 | 0.1422027 |
| 0 | 23 | -0.2489924 |
| 0 | 24 | -0.0832828 |
we_ttest_baseline <- mapply(we_my.t, 0, seq(1,24,1),SIMPLIFY=FALSE) #compare t (first parathese) to t[i] (second parantheses) increasing by 1
baseline_ttest(we_ttest_baseline)| t | t + 1 | t-value | Degrees of Freedom | p-value |
|---|---|---|---|---|
| 0 | 1 | 0.5717846 | 1161.88366 | 5.675785e-01 |
| 0 | 2 | 1.5919359 | 1008.44599 | 1.117125e-01 |
| 0 | 3 | -1.0685461 | 214.74566 | 2.864739e-01 |
| 0 | 4 | 0.6153736 | 1116.22594 | 5.384335e-01 |
| 0 | 5 | 0.9396382 | 979.10341 | 3.476349e-01 |
| 0 | 6 | -1.1795694 | 280.31623 | 2.391716e-01 |
| 0 | 7 | -0.2036380 | 1067.87587 | 8.386752e-01 |
| 0 | 8 | 0.6497173 | 972.54306 | 5.160283e-01 |
| 0 | 9 | -0.6307460 | 351.28994 | 5.286168e-01 |
| 0 | 10 | -0.9676900 | 309.04342 | 3.339559e-01 |
| 0 | 11 | -0.9267542 | 1073.79066 | 3.542624e-01 |
| 0 | 12 | -0.4001689 | 1197.17272 | 6.891035e-01 |
| 0 | 13 | 3.3603937 | 676.58875 | 8.220450e-04 |
| 0 | 14 | 5.6600766 | 890.33555 | 2.040178e-08 |
| 0 | 15 | 0.4231819 | 395.82346 | 6.723924e-01 |
| 0 | 16 | 3.3898118 | 317.82041 | 7.875779e-04 |
| 0 | 17 | 5.1355858 | 889.19740 | 3.456716e-07 |
| 0 | 18 | -0.7164395 | 361.98379 | 4.741820e-01 |
| 0 | 19 | 2.3093761 | 191.37725 | 2.199015e-02 |
| 0 | 20 | 4.1802245 | 873.54302 | 3.205482e-05 |
| 0 | 21 | 0.8666887 | 390.06057 | 3.866454e-01 |
| 0 | 22 | 0.2287513 | 64.77158 | 8.197829e-01 |
| 0 | 23 | 2.5427088 | 1081.13071 | 1.113820e-02 |
| 0 | 24 | 2.2872647 | 1080.95421 | 2.237292e-02 |
we_D_baseline <- mapply(we_my.d, 0, seq(1,24,1),SIMPLIFY=FALSE)
baseline_cohen_d(we_D_baseline)| t | t + 1 | Cohen’s d |
|---|---|---|
| 0 | 1 | 0.0334412 |
| 0 | 2 | 0.0777773 |
| 0 | 3 | -0.0966754 |
| 0 | 4 | 0.0362120 |
| 0 | 5 | 0.0468851 |
| 0 | 6 | -0.0989057 |
| 0 | 7 | -0.0122764 |
| 0 | 8 | 0.0321927 |
| 0 | 9 | -0.0482579 |
| 0 | 10 | -0.0764371 |
| 0 | 11 | -0.0478523 |
| 0 | 12 | -0.0216259 |
| 0 | 13 | 0.2228626 |
| 0 | 14 | 0.2873740 |
| 0 | 15 | 0.0326963 |
| 0 | 16 | 0.2635803 |
| 0 | 17 | 0.2566654 |
| 0 | 18 | -0.0557482 |
| 0 | 19 | 0.2039772 |
| 0 | 20 | 0.2102911 |
| 0 | 21 | 0.0657068 |
| 0 | 22 | 0.0270689 |
| 0 | 23 | 0.1373736 |
| 0 | 24 | 0.1204946 |
we_mean_baseline <- mapply(we_mean, 0, seq(1,24,1)) # comparing time zero [3/2020]across all of the months
baseline_mean_diff(we_mean_baseline)| t | t+1 | Mean Difference |
|---|---|---|
| 0 | 1 | 0.0530735 |
| 0 | 2 | 0.1226640 |
| 0 | 3 | -0.1575023 |
| 0 | 4 | 0.0544833 |
| 0 | 5 | 0.0717923 |
| 0 | 6 | -0.1604908 |
| 0 | 7 | -0.0190853 |
| 0 | 8 | 0.0495303 |
| 0 | 9 | -0.0765531 |
| 0 | 10 | -0.1217559 |
| 0 | 11 | -0.0731274 |
| 0 | 12 | -0.0334520 |
| 0 | 13 | 0.3443412 |
| 0 | 14 | 0.4206792 |
| 0 | 15 | 0.0530747 |
| 0 | 16 | 0.4180032 |
| 0 | 17 | 0.3814797 |
| 0 | 18 | -0.0895754 |
| 0 | 19 | 0.3272803 |
| 0 | 20 | 0.3089956 |
| 0 | 21 | 0.1048303 |
| 0 | 22 | 0.0439469 |
| 0 | 23 | 0.2022358 |
| 0 | 24 | 0.1812803 |
Analytic <- ggplot(data=df2, aes(x=Date_mean, y=Analytic_mean, group=1)) +
geom_line(colour = "dodgerblue3") +
scale_x_date(date_breaks = "3 month", date_labels = "%Y-%m") +
geom_ribbon(aes(ymin=Analytic_mean-Analytic_std.error, ymax=Analytic_mean+Analytic_std.error), alpha=0.2) +
ggtitle("Analytic Thinking") +
labs(x = "Month", y = 'Standardized score') +
plot_aes + #here's our plot aes object
geom_vline(xintercept = as.numeric(as.Date("2020-03-01")), linetype = 1) +
geom_rect(data = df2, #summer surge
aes(xmin = as.Date("2020-06-15", "%Y-%m-%d"),
xmax = as.Date("2020-07-20", "%Y-%m-%d"),
ymin = -Inf,
ymax = Inf),
fill = "gray",
alpha = 0.009) +
geom_rect(data = df2, #winter surge
aes(xmin = as.Date("2020-11-15", "%Y-%m-%d"),
xmax = as.Date("2021-01-01", "%Y-%m-%d"),
ymin = -Inf,
ymax = Inf),
fill = "gray",
alpha = 0.009)
Analytic <- Analytic + annotate(geom="text",x=as.Date("2020-07-01"),
y=43,label="Summer 2020 surge", size = 3) +
annotate(geom="text",x=as.Date("2020-12-03"),
y=43,label="Winter 2020 surge", size = 3)
AnalyticCogproc <- ggplot(data=df2, aes(x=Date_mean, y=cogproc_mean, group=1)) +
geom_line(colour = "dodgerblue3") +
scale_x_date(date_breaks = "3 month", date_labels = "%Y-%m") +
geom_ribbon(aes(ymin=cogproc_mean-cogproc_std.error, ymax=cogproc_mean+cogproc_std.error), alpha=0.2) +
ggtitle("Cognitive Processing") +
labs(x = "Month", y = '% Total Words') +
plot_aes + #here's our plot aes object
geom_vline(xintercept = as.numeric(as.Date("2020-03-01")), linetype = 1) +
geom_rect(data = df2, #summer surge
aes(xmin = as.Date("2020-06-15", "%Y-%m-%d"),
xmax = as.Date("2020-07-20", "%Y-%m-%d"),
ymin = -Inf,
ymax = Inf),
fill = "gray",
alpha = 0.009) +
geom_rect(data = df2, #winter surge
aes(xmin = as.Date("2020-11-15", "%Y-%m-%d"),
xmax = as.Date("2021-01-01", "%Y-%m-%d"),
ymin = -Inf,
ymax = Inf),
fill = "gray",
alpha = 0.009)
Cogproc <- Cogproc + annotate(geom="text",x=as.Date("2020-07-01"),
y=12.5,label="Summer 2020 surge", size = 3) +
annotate(geom="text",x=as.Date("2020-12-03"),
y=12.5,label="Winter 2020 surge", size = 3)
Cogproci <- ggplot(data=df2, aes(x=Date_mean, y=i_mean, group=1)) +
geom_line(colour = "dodgerblue3") +
scale_x_date(date_breaks = "3 month", date_labels = "%Y-%m") +
geom_ribbon(aes(ymin=i_mean-i_std.error, ymax=i_mean+i_std.error), alpha=0.2) +
ggtitle("I-usage") +
labs(x = "Month", y = '% Total Words') +
plot_aes + #here's our plot aes object
geom_vline(xintercept = as.numeric(as.Date("2020-03-01")), linetype = 1) +
geom_rect(data = df2, #summer surge
aes(xmin = as.Date("2020-06-15", "%Y-%m-%d"),
xmax = as.Date("2020-07-20", "%Y-%m-%d"),
ymin = -Inf,
ymax = Inf),
fill = "gray",
alpha = 0.009) +
geom_rect(data = df2, #winter surge
aes(xmin = as.Date("2020-11-15", "%Y-%m-%d"),
xmax = as.Date("2021-01-01", "%Y-%m-%d"),
ymin = -Inf,
ymax = Inf),
fill = "gray",
alpha = 0.009)
i <- i + annotate(geom="text",x=as.Date("2020-07-01"),
y=1.95,label="Summer 2020 surge", size = 3) +
annotate(geom="text",x=as.Date("2020-12-03"),
y=1.95,label="Winter 2020 surge", size = 3)
iwe <- ggplot(data=df2, aes(x=Date_mean, y=we_mean, group=1)) +
geom_line(colour = "dodgerblue3") +
scale_x_date(date_breaks = "3 month", date_labels = "%Y-%m") +
geom_ribbon(aes(ymin=we_mean-we_std.error, ymax=we_mean+we_std.error), alpha=0.2) +
ggtitle("We-usage") +
labs(x = "Month", y = '% Total Words') +
plot_aes + #here's our plot aes object
geom_vline(xintercept = as.numeric(as.Date("2020-03-01")), linetype = 1) +
geom_rect(data = df2, #summer surge
aes(xmin = as.Date("2020-06-15", "%Y-%m-%d"),
xmax = as.Date("2020-07-20", "%Y-%m-%d"),
ymin = -Inf,
ymax = Inf),
fill = "gray",
alpha = 0.009) +
geom_rect(data = df2, #winter surge
aes(xmin = as.Date("2020-11-15", "%Y-%m-%d"),
xmax = as.Date("2021-01-01", "%Y-%m-%d"),
ymin = -Inf,
ymax = Inf),
fill = "gray",
alpha = 0.009)
we <- we + annotate(geom="text",x=as.Date("2020-07-01"),
y=6.5,label="Summer 2020 surge", size = 3) +
annotate(geom="text",x=as.Date("2020-12-03"),
y=6.5,label="Winter 2020 surge", size = 3)
wegraphs <- ggpubr::ggarrange(Analytic,Cogproc,i,we,ncol=2, nrow=2, common.legend = TRUE, legend = "bottom")
annotate_figure(graphs,
top = text_grob("CEOs' Language Change", color = "black", face = "bold", size = 20),
bottom = text_grob("Note. Vertical Line Represents the onset of the pandemic. \n\ Horizontal shading represents Standard Error. Vertical bars represent virus surges."
, color = "Black",
hjust = 1.1, x = 1, face = "italic", size = 16))